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Method to identify attribute type and filter state in a data analysis environment

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method that can be universally applied to user interfaces dealing with lists of data sets, either categorical or quantitative, in spreadsheet columns or dropdown menus. This solution enables users to quickly identify criteria for data type and filtering state.

Country

Undisclosed

Language

English (United States)

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Method to identify attribute type and filter state in a data analysis environment

In traditional spreadsheet programs and analysis environments, users assign attributes to visual aspects of charts such as the x/y axes and the size or color of items in the chart. The filtering of these data values is fundamental to the analysis process as users rarely visualize a data set in its totality. In addition, before the analysis starts, users assign attributes to dimensions and set measures (parameters) that will be used in the measure dimension of a chart. This is known as modeling, a pre-requisite to the analysis process.

A success measure for meaningful analysis is the fluent use of controls to filter and re -arrange data to create different visualizations that can keep up with the user's speed of thought. This means that incomprehensible displays or confusing user experiences can limit the depth of the analysis and may introduce errors.

Existing environments of analysis lack visual cues that allow a user to rapidly identify data types and filter states under collapsed menus or long spreadsheets. A fast and efficient method is needed to identify data types and filter states .

The novel contribution is a set of visual rules that can be universally applied to user interfaces dealing with lists of data sets in spreadsheet columns or dropdown menus. This solution enables users to quickly identify criteria for data type and filtering state.

The data type is categorical or quantitative. Quickly identifying the category of attributes can help analysts reduce the friction between raw data and building visualizations. In addition, identifying the category informs the users of the available filtering options for each specific data set (e.g., check boxes for categorical data, sliders for quantitative and temporal data, etc.)

For the filtering state, in addition to identifying whether a column in a spreadsheet has been filtered , the solution allows analysts to roughly assess the amount of filtered data and the general position of the data in the column . This insight, while not...